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                    <h2 id="get-started-with-tensorflow">
      Get Started with TensorFlow</h2>
    
    

                  <div class="devsite-landing-row-description">
            <p>
  TensorFlow is an open-source machine learning library for research and
  production. TensorFlow offers APIs for beginners and experts to develop
  for desktop, mobile, web, and cloud. See the sections below to get
  started.
</p>

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      <div class="devsite-landing-row-item-code">
  <pre class="prettyprint">
import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
</pre>
<div class="colab-callout-footer">
  <a class="button" target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb">Run code now</a>
  <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb"><span>Try in Google's interactive notebook</span></a>
</div>

</div>
  </div>


        <div class="devsite-landing-row-item-description"
         >

      

      <div class="devsite-landing-row-item-body">
        
                    <h3 id="learn-and-use-ml">
      Learn and use ML</h3>
    
    

                  <div class="devsite-landing-row-item-description-content">
            <p>
  The high-level Keras API provides building blocks to create and
  train deep learning models. Start with these beginner-friendly
  notebook examples, then read the
  <a href="https://www.tensorflow.org/guide/keras">TensorFlow Keras guide</a>.
</p> <ol style="padding-left: 20px;">
  <li><a href="https://www.tensorflow.org/tutorials/keras/basic_classification">Basic classification</a></li>
  <li><a href="https://www.tensorflow.org/tutorials/keras/basic_text_classification">Text classification</a></li>
  <li><a href="https://www.tensorflow.org/tutorials/keras/basic_regression">Regression</a></li>
  <li><a href="https://www.tensorflow.org/tutorials/keras/overfit_and_underfit">Overfitting and underfitting</a></li>
  <li><a href="https://www.tensorflow.org/tutorials/keras/save_and_restore_models">Save and load</a></li>
</ol>

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      <a
          href="https://www.tensorflow.org/guide/keras"
              class="tfo-button-primary button button-primary"
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    Read the Keras guide
  </a>
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                    <h3 id="research-and-experimentation">
      Research and experimentation</h3>
    
    

                  <div class="devsite-landing-row-item-description-content">
            <p>
  Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with
  these notebooks, then read the <a href="https://www.tensorflow.org/guide/eager">eager execution guide</a>.
</p>
<ol style="padding-left: 20px;">
  <li>
    <a href="https://www.tensorflow.org/tutorials/eager/eager_basics">Eager execution basics</a>
  </li>
  <li>
    <a href="https://www.tensorflow.org/tutorials/eager/automatic_differentiation">Automatic differentiation and gradient tape</a>
  </li>
  <li>
    <a href="https://www.tensorflow.org/tutorials/eager/custom_training">Custom training: basics</a>
  </li>
  <li>
    <a href="https://www.tensorflow.org/tutorials/eager/custom_layers">Custom layers</a>
  </li>
  <li>
    <a href="https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough">Custom training: walkthrough</a>
  </li>
</ol>

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      <a
          href="https://www.tensorflow.org/guide/eager"
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                    <h3 id="ml-at-production-scale">
      ML at production scale</h3>
    
    

                  <div class="devsite-landing-row-item-description-content">
            <p>
  Estimators can train large models on multiple machines in a
  production environment. TensorFlow provides a collection of
  pre-made Estimators to implement common ML algorithms. See the
  <a href="https://www.tensorflow.org/guide/estimators">Estimators guide</a>.
</p> <ol style="padding-left: 20px;">
  <li><a href="https://www.tensorflow.org/tutorials/estimators/linear">Build a linear model with Estimators</a></li>
  <li><a href="https://github.com/tensorflow/models/tree/master/official/wide_deep" class="external">Wide and deep learning with Estimators</a></li>
  <li><a href="https://github.com/tensorflow/models/tree/master/official/boosted_trees" class="external">Boosted trees</a></li>
  <li><a href="https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub">How to build a simple text classifier with TF-Hub</a></li>
  <li><a href="https://www.tensorflow.org/tutorials/estimators/cnn">Build a Convolutional Neural Network using Estimators</a></li>
</ol>

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                  <div class="devsite-landing-row-item-buttons">
      <a
          href="https://www.tensorflow.org/guide/estimators"
              class="tfo-button-primary button button-primary"
            >
    Read the Estimators guide
  </a>
  </div>
              </div>
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</div>
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  </section>
        
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                    <h2 id="google-colab58-an-easy-way-to-learn-and-use-tensorflow">
      Google Colab&#58; An easy way to learn and use TensorFlow</h2>
    
    

                  <div class="devsite-landing-row-description">
            <p>
  <a href="https://colab.research.google.com/notebooks/welcome.ipynb" class="external">Colaboratory</a>
  is a Google research project created to help disseminate machine learning
  education and research. It's a Jupyter notebook environment that requires
  no setup to use and runs entirely in the cloud.
  <a href="https://medium.com/tensorflow/colab-an-easy-way-to-learn-and-use-tensorflow-d74d1686e309" class="external">Read the blog post</a>.
</p>

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                    <h2 id="build-your-first-ml-app">
      Build your first ML app</h2>
    
    

                  <div class="devsite-landing-row-description">
            <p>Create and deploy TensorFlow models on web and mobile.</p>

          </div>
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              <a href="https://js.tensorflow.org">
        <h3 id="web-developers">
      Web developers</h3>
    
  </a>
    

                  <div class="devsite-landing-row-item-description-content">
            TensorFlow.js is a WebGL accelerated, JavaScript library to train and deploy ML models in the browser and for Node.js.

          </div>
        
        
              </div>
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</div>
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              <a href="https://www.tensorflow.org/lite/">
        <h3 id="mobile-developers">
      Mobile developers</h3>
    
  </a>
    

                  <div class="devsite-landing-row-item-description-content">
            TensorFlow Lite is lightweight solution for mobile and embedded devices.

          </div>
        
        
              </div>
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</div>
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                    <h2 id="videos-and-updates">
      Videos and updates</h2>
    
    

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            <p>
  Subscribe to the TensorFlow
  <a href="https://www.youtube.com/tensorflow" class="external">YouTube channel</a>
  and <a href="https://blog.tensorflow.org" class="external">blog</a> for
  the latest videos and updates.
</p>

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                    <h3 id="tfdata-fast-flexible-and-easy-to-use-input-pipelines">
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